Abstract: A computer-implemented method, computer program product, and computing system for monitoring a plurality of encounter participants is executed on a computing device and includes obtaining encounter information of a user encounter. The encounter information is processed to: associate at least a first portion of the encounter information with at least one known encounter participant, and associate at least a second portion of the encounter information with at least one unknown encounter participant. A computer-implemented method, computer program product, and computing system for populating records via semantic frames is executed on a computing device and includes obtaining encounter information of a user encounter. The encounter information is processed to generate an encounter transcript.

Abstract: A method, computer program product, and computing system for populating medical records via semantic frames is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is processed to generate an encounter transcript. At least one semantic frame is generated based, at least in part, upon at least one portion of the encounter transcript, wherein the at least one semantic frame defines an abstract meaning for the at least one portion of the encounter transcript.

Abstract: Techniques for entity detection include matching a token from at least a portion of a text string with a matching concept in an ontology, wherein the at least a portion of the text string has been labeled as corresponding to a particular entity type. A first concept may be identified as being hierarchically related to the matching concept within the ontology, and a second concept may be identified as being hierarchically related to the first concept within the ontology. Based at least in part on the labeling of the at least a portion of the text string as corresponding to the particular entity type, a statistical model may be trained to associate the first concept with a first probability of corresponding to the particular entity type and the second concept with a second probability of corresponding to the particular entity type.

Abstract: Techniques for entity detection include matching a token from at least a portion of a text string with a matching concept in an ontology. A first concept may be identified as being hierarchically related to the matching concept within the ontology, and a second concept may be identified as being hierarchically related to the first concept within the ontology. The first and second concepts may be included in a set of features of the token. Based at least in part on the set of features of the token, a measure related to a likelihood that the at least a portion of the text string corresponds to a particular entity type may be determined.

Abstract: Techniques for entity detection include matching a token from at least a portion of a text string with a matching concept in an ontology, wherein the at least a portion of the text string has been labeled as corresponding to a particular entity type. A first concept may be identified as being hierarchically related to the matching concept within the ontology, and a second concept may be identified as being hierarchically related to the first concept within the ontology. Based at least in part on the labeling of the at least a portion of the text string as corresponding to the particular entity type, a statistical model may be trained to associate the first concept with a first probability of corresponding to the particular entity type and the second concept with a second probability of corresponding to the particular entity type.

Abstract: Techniques for entity detection include matching a token from at least a portion of a text string with a matching concept in an ontology. A first concept may be identified as being hierarchically related to the matching concept within the ontology, and a second concept may be identified as being hierarchically related to the first concept within the ontology. The first and second concepts may be included in a set of features of the token. Based at least in part on the set of features of the token, a measure related to a likelihood that the at least a portion of the text string corresponds to a particular entity type may be determined.